Spring 2019

February 19, 2019 to February 20, 2019

Safeguarding Privacy in Dynamic Decision-Making Problems

Speaker: Kuang Xu (Stanford University)

The increasing ubiquity of large-scale infrastructures for surveillance and data analysis has made understanding the impact of privacy a pressing priority in many domains. We propose a framework for studying a fundamental cost vs. privacy...

February 26, 2019 to February 27, 2019

Coded Computing: A Transformative Framework for Resilient, Secure, and Private Distributed Learning

Speaker: Salman Avestimehr (University of Southern California)

This talk introduces "Coded Computing”, a new framework that brings concepts and tools from information theory and coding into distributed computing to mitigate several performance bottlenecks that arise in large-scale distributed computing...

March 12, 2019 to March 13, 2019

Automatic Computation of Exact Worst-Case Performance for First-Order Methods

Speaker: Julien Hendrickx (UCLouvain)

Joint work with Adrien Taylor (INRIA) and Francois Glineur (UCLouvain). We show that the exact worst-case performances of a wide class of first-order convex optimization algorithms can be obtained as solutions to semi-definite programs,...

April 9, 2019 to April 10, 2019

Personalized Dynamic Pricing with Machine Learning: High Dimensional Covariates and Heterogeneous Elasticity

Speaker: Gah-Yi Ban (London Business School)

We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers’ characteristics encoded as a $d$-dimensional feature vector. We assume a personalized demand...

April 23, 2019 to April 24, 2019

Memory-Efficient Adaptive Optimization for Humungous-Scale Learning

Speaker: Yoram Singer (Princeton University & Google)

Adaptive gradient-based optimizers such as AdaGrad and Adam are among the methods of choice in modern machine learning. These methods maintain second-order statistics of each model parameter, thus doubling the memory footprint of the...

April 30, 2019 to May 1, 2019

On Coupling Methods for Nonlinear Filtering and Smoothing

Speaker: Youssef Marzouk (MIT)

Bayesian inference for non-Gaussian state-space models is a ubiquitous problem with applications ranging from geophysical data assimilation to mathematical finance. We will discuss how deterministic couplings between probability...

May 14, 2019 to May 15, 2019

Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery

Speaker: Mihaela van der Schaar (University of Cambridge)

The overarching goal of my research is to develop cutting-edge machine learning, AI and operations research theory, methods, algorithms, and systems to understand the basis of health and disease; develop methodology to catalyze clinical...